Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse

FPGA-based fast bin-ratio spiking ensemble network for radioisotope identification

In this work, we demonstrate the training, conversion, and implementation flow of an FPGA-based bin-ratio ensemble spiking neural network applied for radioisotope identification. The combination of techniques including learned step quantisation (LSQ) and pruning facilitated the implementation by compressing the network's parameters down to 30% yet retaining the accuracy of 97.04% with an accuracy loss of less than 1%. Meanwhile, the proposed ensemble network of 20 3-layer spiking neural networks (SNNs), which incorporates 1160 spiking neurons, only needs 334 μs for a single inference with the given clock frequency of 100 MHz. Under such optimisation, this FPGA implementation in an Artix-7 board consumes 157 μJ per inference by estimation.

E-commerce channel management on the manufacturers’ side: ongoing debates and future research pathways

Since the beginning of the 2000s, online commerce has been gradually taking over and shaping the global marketplace. This has led several scholars to study the phenomenon from different angles, from consumer habits to privacy risks to related technological innovations. However, only recently has a branch of literature addressing the online channel management phenomenon from the manufacturers’ perspective emerged. This rapidly expanding literature strand remains rather fragmented, raising the need for a systematic literature review to comprehensively structure and discuss it. This study, accordingly, proposes a systematic literature review on online channel management from the manufacturers’ perspective. Firstly, it provides relevant bibliometric insights into the ongoing research on the topic. Secondly, applying the bibliographic coupling methodology individuates 92 interconnected contributions published by 31 December 2021. Three different, albeit interconnected, thematic clusters are discovered and reviewed, revealing their focus on (a) strategic marketing issues around manufacturervsretailer conflict, (b) pricing policies and trade-offs among pricevsservices, and (c) operational interactions and strategies between supply chain members. Finally, after a systematic literature review the authors develop thirteen original research propositions concerning new research pathways and theoretical advancements to be designed and implemented.

Artificial intelligence and work: a critical review of recent research from the social sciences

This review seeks to present a comprehensive picture of recent discussions in the social sciences of the anticipated impact of AI on the world of work. Issues covered include: technological unemployment, algorithmic management, platform work and the politics of AI work. The review identifies the major disciplinary and methodological perspectives on AI’s impact on work, and the obstacles they face in making predictions. Two parameters influencing the development and deployment of AI in the economy are highlighted: the capitalist imperative and nationalistic pressures.

Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap

The rapid advancement of digital technologies has fundamentally changed the competitive dynamics of the logistics industry. For players in the logistics industry, digitization has become an unavoidable situation to achieve survival and sustainable competitiveness. A technology strategy is essential for digitization, and identifying opportunities and threats of technology development through technology trend exploration is important for technology strategy. In addition, to enable the implementation of the technology strategy, it is necessary to detect the change in technology and search for the technology that is expected to have a practical development effect. The purpose of this study is to identify opportunities and areas for technology development through patent data in establishing technology strategies. Previous research mainly relied on the expert interview method, and there was also a patent analysis study based on topic modeling, but only to grasp technology trends. This paper aims to propose a new framework for the extension to the stage for establishing a technology roadmap. By using the Word2Vec algorithm, we will investigate the patent search formula that reflects the trend, the prediction of changes in logistics technology through LDA (Latent Dirichlet Allocation) clustering of patent data, and the derivation of vacant technology by experimental methods. The proposed framework is expected to be utilized for predicting technological change and deriving promising technologies for establishing technology roadmaps in logistics companies.

Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes

In recent years artificial intelligence (AI) has been seen as a technology with tremendous potential for enabling companies to gain an operational and competitive advantage. However, despite the use of AI, businesses continue to face challenges and are unable to immediately realize performance gains. Furthermore, firms need to introduce robust AI systems and mitigate AI risks, which emphasizes the importance of creating suitable AI governance practices. This study, explores how AI governance is applied to promote the development of robust AI applications that do not introduce negative effects, based on a comparative case analysis of three firms in the energy sector. The study illustrates which practices are placed to produce knowledge that assists with decision making while at the same time overcoming barriers with recommended actions leading to desired outcomes. The study contributes by exploring the main dimensions relevant to AI's governance in organizations and by uncovering the practices that underpin them.

Supply chain resilience in the UK during the coronavirus pandemic: A resource orchestration perspective

The COVID-19 pandemic caused significant disruptions to global operations and supply chains. While the huge impact of the pandemic has nurtured important literature over the last couple of years, little is being said about the role of resource orchestration in supporting resilience in highly disruptive contexts. Thus, this study aims to this knowledge gap by proposing an original model to explore supply chain resilience (SCRE) antecedents, considering supply chain alertness (SCAL) as a central point to support resilience. This study focuses on the resource orchestration theory (ROT) to design a conceptual model. The partial least squares structural equation modeling (PLS-SEM) served to validate the model, exploring data from the UK supply chain decision-makers. The study reveals a number of both expected and unexpected findings. These include the evidence that supply chain disruption orientation (SCDO) has a strong positive effect on the SCAL. In addition, SCAL plays a strong positive effect in resource reconfiguration (RREC), supply chain efficiency (SCEF) and SCRE. We further identified a partial mediation effect of RREC on the relationship between SCAL and SCRE. Surprisingly, it appeared that SCAL strongly influences SCEF, while SCEF itself does not create any significant effect on SCRE. For managers and practitioners, the importance of resource orchestration as a decisive approach to adequately respond to huge disruptions is clearly highlighted by our results. Finally, this paper helps to grasp better how important resource orchestration in operations and supply chains remains for appropriate responses to high disruptions such as the COVID-19 impacts.

Automatic Eligibility of Sellers in an Online Market Place: A Case Study of Amazon Algorithm

Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.